The present invention relates to a method for recognising characters traced manually on an input zone and an electronic device for implementing the same.
Within the scope of the present invention, “character” means any alphanumerical character (A, B, C, . . . , 1, 2, 3 . . . ), punctuation mark (?, !, :, . . . ) or symbolic character (+, −, *, #, . . . ) capable of being traced manually by means of a finger or a tracing instrument, such as a stylus for example. By extension, it will also mean any trace other than the trace of an alphanumerical or symbolic character, with which a particular command is associated, such a trace being defined in the following description as a control character. These control characters are particularly intended to allow certain particular commands such as movement of a cursor, deletion of a previously inputted character, switching between upper and lower case letters, etc.
Various solutions allowing recognition of characters traced manually on an input zone are already known. Such solutions are particularly used for allowing manual input of data, without using a keyboard, in electronic devices, particularly portable electronic devices such as electronic diaries, pocket computers or electronic timepieces.
One solution consists in providing complex recognition algorithms based on a dot matrix or bitmap image of the traced character, which is compared to the model data of a predetermined set of characters in order to identify the character closest to the trace performed. This solution is impractical and often much too heavy and demanding in terms of computational time and power to be applied, particularly in portable electronic devices. Another drawback of this solution lies in the fact that it requires a large memory space for defining the characters to be recognised. This solution further requires learning on the part of the user so that he traces each character in accordance with a determined pattern.
Thus, the current practice is to propose recognition devices and algorithms wherein one examines the movement performed when the character is traced on the input zone. Typically, the signals emanating from the input zone, or more exactly from the plurality of sensors forming this input zone, are sampled in order to determine the history of evolution of the trace of the character.
Document No. EP 0 695 979 in the name of the Applicant discloses for example a device for identifying a character traced on a surface as well as a process for identifying such a character, particularly for a watch. According to this document, a series of identifiers representative of changes of state (“ON” or “OFF”) in the sensors forming the input zone is produced and compared to at least one series of reference identifiers in order to identify the traced character.
Given the complexity and the strong similarity of certain characters, the current practice is to adopt certain simplifications in the set of characters (for example breaking down the characters into different sets of alphabetic characters, numerical characters, symbolic characters, etc., particularly in order to avoid confusion between certain characters such as “O” and “0”, “B” and “8”, “+” and “T”, etc.) and to force the user to trace the characters in accordance with well determined sequences which are sometimes contrary to his normal practice or even any normal practice. Thus, with the existing solutions, there is often only one possible way of tracing each character so that it is not confused with others. Moreover, the sequences which have to be adopted do not necessarily correspond to the natural trace of the desired character. These difficulties are even greater, given that handwriting characteristics are greatly variable and even completely opposite, for example between the handwriting of a right-handed person and a left-handed person.
Other examples of character recognition devices with similar constraints are also disclosed in document Nos. U.S. Pat. No. 4,047,010 A, U.S. Pat. No. 4,199,751 A, GB 2,092,352 A and WO 90/15399.
These known recognition devices and algorithms thus require learning on the part of the user or involve constraints for the user. If this learning is imperfect or if the constraints are not followed, errors can appear when the desired text is inputted, which errors extend the time necessary for data input.
Another solution is disclosed in document No. FR 2,538,581. According to this other solution, each stroke performed when a character is traced on the input zone is broken down into a plurality of rectilinear segments and the orientation of each of these segments is determined with respect to a plurality of reference vectors (eight in number). A model of the traced character formed of a determined set of rectilinear segments is thus elaborated then compared to a corresponding set of reference characters. According to document No. FR 2,538,581, this solution is advantageously implemented in a portable object of similar style to a wristwatch.
Document No. EP 0 632 401 proposes modelling each character, not only on the basis of models formed of rectilinear segments, but also on the basis of models taking account of other parameters such as the movement up (“PEN UP”) or down (“PEN DOWN”) of the writing instrument on the input zone, the duration of a pause during tracing, the centre of gravity of the trace, the size of the trace, the rotational direction of a curved segment, the position of the start and end points of a curved segment and the orientation of such a curved segment. This latter document teaches that such a solution is implemented in a parallel processor system using a specific memory type called “associative” memory. Such a parallel processor structure is of course inconceivable for application to portable electronic devices where the data processing capabilities, in particular, are limited.
It will be noted that the solutions proposed in document No. FR 2,538,581 and document No. EP 0 632 401 both rely on geometrical considerations to allow character recognition. This is a major drawback in the sense that it is not practical to model all the possible variants of a given written character by geometrical parameters. Indeed, handwriting can vary greatly from one person to another or even for a single person. Given their great variability, parameters such as the orientation, size, position, length or other geometrical parameters of the trace of the character, can thus not be used to allow truly reliable and robust character recognition.